mpc_python_learn/mpc_pybullet_demo/mpcpy/cvxpy_mpc.py

147 lines
4.1 KiB
Python
Executable File

import numpy as np
np.seterr(divide="ignore", invalid="ignore")
from scipy.integrate import odeint
from scipy.interpolate import interp1d
import cvxpy as opt
from .utils import *
from .mpc_config import Params
P = Params()
def get_linear_model_matrices(x_bar, u_bar):
"""
Computes the LTI approximated state space model x' = Ax + Bu + C
"""
x = x_bar[0]
y = x_bar[1]
v = x_bar[2]
theta = x_bar[3]
a = u_bar[0]
delta = u_bar[1]
ct = np.cos(theta)
st = np.sin(theta)
cd = np.cos(delta)
td = np.tan(delta)
A = np.zeros((P.N, P.N))
A[0, 2] = ct
A[0, 3] = -v * st
A[1, 2] = st
A[1, 3] = v * ct
A[3, 2] = v * td / P.L
A_lin = np.eye(P.N) + P.DT * A
B = np.zeros((P.N, P.M))
B[2, 0] = 1
B[3, 1] = v / (P.L * cd**2)
B_lin = P.DT * B
f_xu = np.array([v * ct, v * st, a, v * td / P.L]).reshape(P.N, 1)
C_lin = (
P.DT
* (
f_xu - np.dot(A, x_bar.reshape(P.N, 1)) - np.dot(B, u_bar.reshape(P.M, 1))
).flatten()
)
# return np.round(A_lin,6), np.round(B_lin,6), np.round(C_lin,6)
return A_lin, B_lin, C_lin
class MPC:
def __init__(self, state_cost, final_state_cost, input_cost, input_rate_cost):
""" """
self.nx = P.N # number of state vars
self.nu = P.M # umber of input/control vars
if len(state_cost) != self.nx:
raise ValueError(f"State Error cost matrix shuld be of size {self.nx}")
if len(final_state_cost) != self.nx:
raise ValueError(f"End State Error cost matrix shuld be of size {self.nx}")
if len(input_cost) != self.nu:
raise ValueError(f"Control Effort cost matrix shuld be of size {self.nu}")
if len(input_rate_cost) != self.nu:
raise ValueError(
f"Control Effort Difference cost matrix shuld be of size {self.nu}"
)
self.dt = P.DT
self.control_horizon = int(P.T / P.DT)
self.Q = np.diag(state_cost)
self.Qf = np.diag(final_state_cost)
self.R = np.diag(input_cost)
self.P = np.diag(input_rate_cost)
self.u_bounds = np.array([P.MAX_ACC, P.MAX_STEER])
self.du_bounds = np.array([P.MAX_D_ACC, P.MAX_D_STEER])
def optimize_linearized_model(
self,
A,
B,
C,
initial_state,
target,
verbose=False,
):
"""
Optimisation problem defined for the linearised model,
:param A:
:param B:
:param C:
:param initial_state:
:param target:
:param verbose:
:return:
"""
assert len(initial_state) == self.nx
# Create variables
x = opt.Variable((self.nx, self.control_horizon + 1), name="states")
u = opt.Variable((self.nu, self.control_horizon), name="actions")
cost = 0
constr = []
for k in range(self.control_horizon):
cost += opt.quad_form(target[:, k] - x[:, k + 1], self.Q)
cost += opt.quad_form(u[:, k], self.R)
# Actuation rate of change
if k < (self.control_horizon - 1):
cost += opt.quad_form(u[:, k + 1] - u[:, k], self.P)
# Kinematics Constrains
constr += [x[:, k + 1] == A @ x[:, k] + B @ u[:, k] + C]
# Actuation rate of change limit
if k < (self.control_horizon - 1):
constr += [
opt.abs(u[0, k + 1] - u[0, k]) / self.dt <= self.du_bounds[0]
]
constr += [
opt.abs(u[1, k + 1] - u[1, k]) / self.dt <= self.du_bounds[1]
]
# Final Point tracking
cost += opt.quad_form(x[:, -1] - target[:, -1], self.Qf)
# initial state
constr += [x[:, 0] == initial_state]
# actuation magnitude
constr += [opt.abs(u[:, 0]) <= self.u_bounds[0]]
constr += [opt.abs(u[:, 1]) <= self.u_bounds[1]]
prob = opt.Problem(opt.Minimize(cost), constr)
solution = prob.solve(solver=opt.OSQP, warm_start=True, verbose=False)
return x, u